Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Background: Nonobstetric surgery is sometimes required during pregnancy, and neck abscess or facial bone fracture surgery cannot be postponed in pregnant women. However, dental surgery can be stressful and can cause inflammation, and the inflammatory response is a well-known major cause of preterm labor. Propofol is an intravenous anesthetic commonly used for general anesthesia and sedation. Studies investigating the effect of propofol on human amnion are rare. The current study investigated the effects of propofol on lipopolysaccharide (LPS)-induced inflammatory responses in human amnion-derived WISH cells. Methods: WISH cells were exposed to LPS for 24 h and co-treated with various concentrations of propofol (0.01–1 μg/ml). Cell viability was measured using the MTT assay. Nitric oxide (NO) production was analyzed using a microassay based on the Griess reaction. The protein expression of cyclooxygenase-2 (COX-2), prostaglandin E2 (PGE 2), p38, and phospho-p38 was analyzed using western blotting. Results: Propofol did not affect the viability and NO production of WISH cells. Co-treatment with LPS and propofol reduced COX-2 and PGE 2 protein expression and inhibited p38 phosphorylation in WISH cells. Conclusion Propofol does not affect the viability of WISH cells and inhibits LPS-induced expression of inflammatory factors. The inhibitory effect of propofol on inflammatory factor expression is likely mediated by the inhibition of p38 activation.

Nilotinib is used for treating patients with imatinib-sensitive or -resistant chronic myeloid leukemia (CML); however, nilotinib-resistant cases have been observed in recent years. In addition, a considerable number of patients receiving nilotinib developed diabetes. Metformin is a front-line drug for the treatment of type 2 diabetes, and several studies have shown that diabetes patients treated with metformin have reduced incidence of cancer. This study aimed to define the effect of metformin on CML cells to determine whether metformin overcomes nilotinib resistance, and to identify novel targets for the treatment of nilotinib resistance. Methods: We observed the effects of metformin and nilotinib on K562 and KU812 human CML cell lines. Nilotinib-resistant CML cell lines were generated by exposing cells to gradually increasing doses of nilotinib. Then, we investigated the driving force that makes resistance to nilotinib and the effect of metformin on the driving force. Results: Sub-toxic doses of metformin enhanced nilotinib efficacy by reducing Bcl-xL expression, which induces apoptosis in CML cells. Next, we generated nilotinib-resistant K562 and KU812 cell lines that overexpressed the c-Jun N-terminal kinase (JNK) gene. JNK silencing by a JNK inhibitor restored sensitivity to nilotinib. Furthermore, metformin was effective in decreasing phosphorylated JNK levels, restoring nilotinib sensitivity. Combined treatment with nilotinib and metformin was more effective than combined treatment with nilotinib and a JNK inhibitor in terms of cell proliferation inhibition. Conclusions: This study suggested that combination therapy with metformin and nilotinib may have clinical benefits of enhancing antileukemia efficacy and overcoming resistance to nilotinib.

Background/Aims: Patients with pancreatic cancer (PC) generally have poor clinical outcomes. Early determination of their prognosis is crucial for developing a therapeutic strategy. Recently, various inflammatory markers have been validated as prognostic indicators for many cancers, including PC. However, few studies have evaluated these markers together. Thus, the purpose of this study was to comprehensively evaluate the value of inflammatory markers as prognostic indicators in patients with advanced PC treated with gemcitabine-based chemotherapy as the first line regimen. Methods: This was a single-center retrospective study evaluating 302 patients with advanced PC who began first line treatment between November 2004 and August 2016. These patients were monitored until June 2017. Survival rates were assessed with univariate and multivariate analyses. Continuous variables were separated using the normal range or ideal cut-off levels determined by receiver operating curve analyses. Results: Among inflammatory markers evaluated, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and C-reactive protein (CRP) to albumin ratio (CRP-albumin ratio) were independent predictors of overall survival (hazard ratio, 1.712, 1.345, and 1.454, respectively). Difference in survival rates was significant (p < 0.001) among three groups divided by the number of marker-related risks. Conclusions Baseline inflammatory markers including NLR, PLR, and CRP-albumin ratio are useful in predicting survival rates in patients with PC. Combining these three markers is proven to be valuable.

BACKGROUND: Preterm labor and miscarriage may occur in stressful situations, such as a surgical operation or infection during pregnancy. Pharyngeal and buccal abscess and facial bone fractures are inevitable dental surgeries in pregnant patients. Remifentanil is an opioid analgesic that is commonly used for general anesthesia and sedation. Nonetheless, no study has investigated the effects of remifentanil on amniotic epithelial cells. This study evaluated the effects of remifentanil on the factors related to uterine contraction and its mechanism of action on amniotic epithelial cells.METHODS: Amniotic epithelial cells were preconditioned at various concentrations of remifentanil for 1 h, followed by 24-h lipopolysaccharide (LPS) exposure. MTT assays were performed to assess the cell viability in each group. The effects of remifentanil on factors related to uterine contractions in amniotic epithelial cells were assessed using a nitric oxide (NO) assay, western blot examinations of the expression of nuclear factor-kappa B (NF-κB), cyclooxygenase 2 (COX2), and prostaglandin E2 (PGE₂), and RT-PCR examinations of the expression of the proinflammatory cytokines interleukin (IL)-1β and tumor necrosis factor-alpha (TNF-α).RESULTS: Remifentanil did not affect viability and nitric oxide production of amniotic epithelial cells. Western blot analysis revealed that remifentanil preconditioning resulted in decreased expressions of NF-κB and PGE2 in the cells in LPS-induced inflammation, and a tendency of decreased COX2 expression. The results were statistically significant only at high concentration. RT-PCR revealed reduced expressions of IL-1β and TNF-α.CONCLUSION: Preconditioning with remifentanil does not affect the viability of amniotic epithelial cells but reduces the expression of factors related to uterine contractions in situations where cell inflammation is induced by LPS, which is an important inducer of preterm labor. These findings provide evidence that remifentanil may inhibit preterm labor in clinical settings.
Abortion, Spontaneous ; Abscess ; Anesthesia, General ; Blotting, Western ; Cell Survival ; Cyclooxygenase 2 ; Cytokines ; Dinoprostone ; Epithelial Cells ; Facial Bones ; Female ; Humans ; Inflammation ; Interleukins ; Lipopolysaccharides ; NF-kappa B ; Nitric Oxide ; Obstetric Labor, Premature ; Pregnancy ; Tumor Necrosis Factor-alpha ; Uterine Contraction